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Record W2657436431 · doi:10.11575/prism/29658

The Information Commons at the University of Calgary: service in transition

2009· article· en· W2657436431 on OpenAlexaboutno aff
Susan Beatty

Bibliographic record

VenuePRISM (University of Calgary) · 2009
Typearticle
Languageen
FieldComputer Science
TopicWeb and Library Services
Canadian institutionsnot available
Fundersnot available
KeywordsService (business)Transition (genetics)CommonsBusinessPolitical scienceChemistryMarketingLaw

Abstract

fetched live from OpenAlex

The Information Commons at the University of Calgary Library (1999) is a facility which provides both reference and technical support. Its successful service model is based on collaboration between the Library and University Information Technologies. The University is in the midst of planning for a new Library (Taylor Family Digital Library) which will house additional collaborative partners. The new Learning Commons will have a broader suite of services. The Learning Commons service model will build on the best of the Information Commons and bring new features which will further support learning and collaboration. This paper will focus on the current operations of the Commons (issues and opportunities), the features (physical and virtual) used to support learning and collaboration, and the lessons learned, as well as identify and discuss the current process of transition. Using the lessons learned, the presentation also points to possible future models of service in the new Learning Commons.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.555
Threshold uncertainty score0.895

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0130.008
Scholarly communication0.0130.007
Open science0.0020.011
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0190.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.158
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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